Following a successful track at the Data for Policy Conference 2024, we’re encouraging further submissions for a special collection (virtual special issue) to be published in Data & Policy later in 2025.  

Jump to:

• Introduction
• Themes
• Timetable
• Why submit to Data & Policy?
• Guest Editors

Introduction

Artificial Intelligence (AI) holds immense potential in revolutionizing healthcare by leveraging comprehensive health datasets to create both public and private value. From enhanced diagnostics and drug development to personalized medicine and more efficient healthcare management, AI applications in health offer promising avenues for progress. Particularly noteworthy is its potential to detect and respond to emerging pandemics, an essential capability for regions like the Global South, where inadequate information has hindered effective responses to crises such as the Covid-19 pandemic, underscoring the importance of equitable access to healthcare. At the core of AI-driven innovation lies the indispensable role of large datasets. For AI in health, these datasets encompass a broad spectrum of health-related information, including individual health status, healthcare service delivery, resource availability, utilization, and costs. More specifically, “Clinical data” captures health information collected during patient care or formal clinical trials, while “Health Administrative data” pertains to data generated through routine healthcare services. The “appification” of health, including well-being and symptom-tracking apps, has blurred the boundaries of what qualifies as a medical device. Data collected and generated by these apps can potentially be used in algorithms for medical purposes, thereby being classified as health-related data.

The rapid expansion of health data collection and analysis of individual-level datasets is transforming how we produce health knowledge. Before, health knowledge was built on testing causal relationships with clinical trials. Now, the focus has shifted to identifying patterns and correlations within large datasets. It has also brought forth pressing challenges that demand comprehensive solutions. Foremost, the sensitivity of health data puts it at risk of misuse, exploitation, or unauthorized exposure, thereby presenting tangible consequences such as denial of insurance coverage, loss of employment opportunities, or even hindrance in securing new employment. Furthermore, such unauthorized exposure may compromise individuals’ autonomy, privacy, and control over their personal information, consequently impacting their sense of dignity and self-formation.

Moreover, the digital economy has exacerbated wealth inequality, with data-driven innovations disproportionately benefiting a select few companies. While individuals often act as primary data providers, companies and institutions exploit contract and trade secret laws to de facto own the collected data. This raises concerns about fair compensation for those contributing to the data’s creation (individuals) or its curation (public institutions). Additionally, personal data can produce both positive and negative effects on other individuals within the same group. Systemic biases may lead to datasets that are incomplete, under representative or inaccurate. Lawfully collected data may be used to train algorithms and draw conclusions about individuals who did not consent to their data’s collection or use in this manner. These circumstances can lead to biased decisions that further marginalize protected groups, such as women, LGBTQI+, and indigenous communities.

The debate surrounding access to large health datasets is particularly significant in countries with universal access to public healthcare systems, where vast amounts of data are generated. Concurrently, initiatives to establish digitalized networks of data collected in such services have the potential to escalate data collection exponentially. However, health-related research is moving from the old way of doing things to a new approach where traditional health organizations, which have deep knowledge and expertise, are now partnering with big tech companies that bring advanced analytical skills and powerful infrastructure. This collaboration is creating health data ecosystems that blur the boundaries between public health data management and sovereign infrastructure. As we traverse this intricate landscape, a profound comprehension of the governance of data becomes imperative in tackling the challenges arising from the widespread access to personal health data. Equally significant is the evaluation of how health data can be utilized to establish trustworthy governance in healthcare, thereby optimizing the benefits for both the public and private sectors in the realm of AI-driven health innovation.” 

Themes

We cordially invite full-paper submissions (up to 6,000 words) to our special issue from governmental organizations, academia, and the private sector addressing the following themes (inspirational rather than exhaustive):

• Governance frameworks for accessing health data for AI innovation - clinical, administrative and health-related data.
• Ethical frameworks guiding the private sector’s use of health-related data to create private value and drive innovation.
• Ethical considerations for governmental institutions in using health data to create public value through AI-driven initiatives.
• Addressing infrastructural, legal, and cultural challenges in providing health data access for AI innovation, especially in the Global South.
• Digitisation and governance of Health data generated by government-run and public healthcare services.
• From e-Health to the “appification” of Health: Challenges in Managing Data from Health and Wellness Apps
• Increasing difficulties in accessing health data due to issues such as intellectual property, technical restrictions, and concerns about how AI is used.
• The lack of global standards and compatibility between different health data systems.
• The challenges that come with the shift in how health knowledge is created: moving from the traditional health-related research model of clinical trials to one that relies on analyzing large sets of health data
• Collaborations between public institutions and tech giants to advance health research, and the complications these partnerships bring.

Timetable

Deadline for submissions: The submission deadline has been updated to 17 February 2025 Friday 30 May 2025

Authors interested in presenting at the Data for Policy Conference - Europe 2025 should submit by 21 February, using the 'Data for Policy Conference Proceedings Paper' article type.

Articles will be published as soon as possible after acceptance and added to a collection page; earlier submission therefore may result in earlier publication. 

Submission process

Authors should submit articles through the Data & Policy ScholarOne site, using the ‘Governance of Health Data for AI Innovation' special collection option when prompted in the submission forum.

Before submission, authors should familarise themselves with the Instructions for Authors. Please feel free to use the LaTeX or Word templates. Note also that we have a template in Overleaf, a cloud-based, which has collaborative features and enables authors to submit directly into the Data & Policy system without having to re-upload files.

Note that Data & Policy publishes the following types of articles, which authors will be prompted to select from on submission:

• Data for Policy Conference Proceedings Paper should be used if you are intending to present at any of the Data for Policy series of Conferences in 2025. 
• Research articles that use rigorous methods that demonstrate how data science can inform or impact policy by, for example, improving situation analysis, predictions, public service design, and/or the legitimacy and/or effectiveness of policy making. Published research articles are typically reviewed by three peer reviewers: two assessing the academic or methodological rigour of the paper; and one providing an interdisciplinary or policy-specific perspective.
• Commentaries are shorter articles that discuss and/or problematize an issue relevant to the Data & Policy scope. Commentaries are typically reviewed by two peer reviewers.
• Translational papers are contributions that show how data science principles, techniques and technologies are being used in practice in organisational settings to improve policy outcomes. They may present original findings but are less embedded in the scholarly literature as research articles. They are typically reviewed by two peer reviewers, who assess the rigour and policy significance of the paper.
• Data papers that provide a structured description of an openly available dataset with the aim of encouraging its re-use for further research.

You can read more on the Instructions for Authors here.

Data & Policy strongly encourages authors to make replication data and code available in an open repository, where this is possible (see the research transparency policy). All authors must provide a Data Availability Statement in their article that explains where the replication material resides, if it is available, and if not, the reason why it cannot be made accessible. Authors who link to replication materials will be awarded Open Data and/or Open Materials badges that display on the published article.

Why submit to Data & Policy?

✔ A venue developed for and expanding the community working at the data science for governance interface, established by the Data for Policy Conference.
✔ Welcomes research, translational articles, commentaries and data papers, plus the Data & Policy blog for more immediate reflections.
✔ Well-cited (2023 Impact Factor: 1.8) and indexed in Web of Science, Scopus and Directory of Open Access Journals.
✔ Open Access with support for authors who do not have access to funding to pay publishing charges.
✔ Promotes open sharing of data and code through Open Science Badges.

Guest Editors

• Renan Gadoni Canaan, Centre for Law, Technology and Society, University of Ottawa, Canada
• Teresa Scassa, Centre for Law, Technology and Society, University of Ottawa, Canada